The advances in Open Set and Open World image classification cannot be trivially adapted to Open Set and Open World object detection, because of a fundamental difference in the problem setting:
The object detector is trained to detect unknown objects as background. Instances of many unknown classes would have been already introduced to the object detector along with known objects. As they are not labelled, these unknown instances would be explicitly learned as background, while training the detection model.
Dhamija [1]} find that even with this extra training signal, the state-of-the-art object detectors result in false positive detections, where the unknown objects end up being classified as one of the known classes, often with very high probability. Miller [2]} propose to use dropout sampling to get an estimate of the uncertainty of the object detection prediction. This is the only peer-reviewed research work in the open set object detection literature.
Our proposed Open World Object Detection goes a step further to incrementally learn the new classes, once they are detected as unknown and an oracle provides labels for the objects of interest among all the unknowns. To the best of our knowledge this has not been tried in the literature.
